Hi welcome to my journey of statistics with R - Subhrojit Nandan

First lesson with Statistics with R

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co-relation does not imply causation, what determines whether we can infer causation or just corelation is the type of study that we’re basing our conclusions. Observational studies for the most part allows us to only make corelational statements.

While experiments allow us to infer causation. It is said for the most parts, because there are actually more advanced methods broadfly titled causal inference that allow for making causal infernces for observational studies.


library(tidyverse)
library(dplyr)
library(ggplot2)
library(statsr)

data(present)
View(present)

arbuthnot <- arbuthnot %>%
  mutate(more_boys = boys > girls)

range(present$year)

arbuthnot <- arbuthnot %>%
  mutate(total = boys + girls)


arbuthnot <- arbuthnot %>%
  mutate(more_boys = boys > girls)

more_boys <- boys > girls
Error: object 'boys' not found
present <- present %>%
  mutate(total = boys + girls)
View(present)

present <- present %>%
  mutate(prop_boys = boys / total)
View(present)

ggplot(data = present, aes(x = year, y = prop_boys)) +
  geom_point() + 
  geom_smooth()

present <- present %>%
  mutate(more_boys = boys > girls)
View(present)

present <- present %>%
  mutate(prop_boy_girl = boys / girls)
View(present)

ggplot(data = present, aes(x = year, y = prop_boy_girl)) +
  geom_point() + 
  geom_smooth()

present <- present %>%
  arrange(desc(total))
View(present)

library(shiny)
Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio


sfo_feb_flights <- nycflights %>%
  filter(dest == "SFO", month == 2)

View(sfo_feb_flights)

rdu_flights %>%
  group_by(origin) %>%
  summarise(mean_dd = mean(dep_delay), sd_dd = sd(dep_delay), n = n())
sfo_feb_flights %>%
  group_by(carrier) %>%
  summarise(median_dd = median(arr_delay), iqr_dd = IQR(arr_delay), n = n())

ggplot(nycflights, aes(x = factor(month), y = dep_delay)) +
  geom_boxplot()

nycflights <- nycflights %>%
  mutate(dep_type = ifelse(dep_delay < 5, "on time", "delayed"))

nycflights %>%
  group_by(origin) %>%
  summarise(ot_dep_rate = sum(dep_type == "on time") / n()) %>%
  arrange(desc(ot_dep_rate))

ggplot(data = nycflights, aes(x = origin, fill = dep_type)) +
  geom_bar()

nycflights %>% 
  select(avg_speed, tailnum)
Error in .f(.x[[i]], ...) : object 'avg_speed' not found
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